The Impact of a School-Based Academic Support Program on Students’ Psychosocial Impairment
Bibliographic record
Abstract
The skills conveyed in high school are essential for success, and evidence suggests that psychosocial well-being is critical to that process. Advancement via Individual Determination (AVID) is an in-school, academic support program that targets underserved students. The current study explored the impact of an intervention program targeting academic achievement on student perceptions of social support and psychosocial impairment. The study was conducted in an under-resourced, majority Latinx high school and the sample included 75 AVID students and 140 demographically matched controls. Hierarchical linear regression analyses were performed to identify the impact of participation in the AVID program on symptoms of emotional and behavioral distress. After controlling for demographics factors and academic achievement, student perceptions of emotional and teacher support explained 7.2% of the variance in psychosocial distress; participation in the AVID intervention was found to have significantly improved the variance accounted for (ΔR2=.02, 5.13, p=.025; R2=.11, F(9,204)=2.90, p=.003). The results of this study indicate that perceived teacher social support and AVID participation were, independently, significantly associated with reduced student psychosocial distress. These findings suggest that interventions targeting the complex mechanisms of school achievement may also have a positive impact on psychosocial impairment beyond student perceptions of social support.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".